Pseudo-random number generators (PRNGs) are used in a wide variety of cryptographic applications. In particular, random outputs of PRNGs are relied upon in generating, for example, (1) unpredictable session identifiers or cookies for online client-server sessions, (2) key generation for symmetric, asymmetric encryption, the Diffie-Hellman key exchange algorithm and digital signature algorithms (DSA), (3) generating nonces in challenge-response authentication mechanisms, (4) producing random padding in cryptographic padding mechanisms such as public key cryptosystems (PKCS-1) and (5) providing random variables for accomplishing secure transmissions via wireless transport layer security (WTLS) and wireless application protocols (WAPs).
Common PRNGs are based on the American National Standards Institute (ANSI) X9.17 standard and are typically used with the Digital Encryption Standard (DES) or 3-DES block ciphers. Other block ciphers, such as Rivest Cipher-5 (RC-5) may also be used. In order to accomplish secure communications, it is desireable that the outputs from the PRNG be unpredictable. If the output of a PRNG becomes predictable, it will, in turn become easier to decipher any communications from a cryptography system employing such a PRNG. Thus, the random nature of a PRNG is an important aspect in maintaining secure communications.
Recently, several studies have determined that PRNGs using the ANSI X9.17 standard may be vulnerable to certain cryptographic attacks. In particular, it has been discovered that if the internal key used by an ANSI X9.17 PRNG becomes known, the PRNG becomes vulnerable to permanent compromise attacks. If an attacker can force input seed values to an ANSI X9.17 PRNG in an adaptive attack, it may be possible to force the PRNG to generate outputs in a partially-predictable manner. In addition, if an internal state of an ANSI X9.17 PRNG becomes known, a backtracking attack may be performed to discover previous secret outputs of the PRNG. See, e.g., Kelsey, J., et al., “Cryptanalytic Attacks on Pseudo-Random Number Generators,” ESORICS '98 Proceedings, Springer-Verlag, 1998, pp. 77–110 and Kelsey, J. et al., “Yarrow-160: Notes and the Design and Analysis of the Yarrow Cryptographic Pseudo-random Number Generator,” Proceedings of the Sixth Annual Workshop on Selected Areas in Cryptography.
Various methods for random number generation have been previously disclosed. See, for example, U.S. Pat. Nos. 6,141,668; 6,065,029; 6,061,703; 6,044,388; 5,983,252; 5,966,313; 5,961,577; 5,872,725; 5,864,491; 5,828,752; and 5,046,036. However, none of these systems provide a sufficient solution to the possible attacks noted above. Accordingly, there is a need for a method and apparatus for pseudo-random number generation which addresses certain deficiencies in prior technologies.